{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:theses-1218"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:theses-1218","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Computer aided analysis of skin lesions","abstract":"Effective screening to detect the skin cancer accurately in the early stage is essential for reducing the mortality of skin cancer. Surface features, such as texture and pigmentation area from the surface, epi-illumination images of the skin lesions have been well correlated to detect skin cancer. An increase in the lesion's subsurface blood volume has been correlated to early diagnosis of malignant melanoma. A method for estimating the optimal features is obtained. The optimal features help in accurately classify the skin lesion in various grades. To make the process faster these optimal features are clustered. The optimal clusters are obtained by genetic algorithm. The optimal cluster centers act as input to the SVM classifier and the kernel parameters are obtained. Finally, parameters of the kernel function are optimized by genetic algorithm, which help in classifying the skin lesions into various grades leading to early diagnosis of skin cancer.","abstract_html":"Effective screening to detect the skin cancer accurately in the early stage is essential for reducing the mortality of skin cancer. Surface features, such as texture and pigmentation area from the surface, epi-illumination images of the skin lesions have been well correlated to detect skin cancer. An increase in the lesion's subsurface blood volume has been correlated to early diagnosis of malignant melanoma. A method for estimating the optimal features is obtained. The optimal features help in accurately classify the skin lesion in various grades. To make the process faster these optimal features are clustered. The optimal clusters are obtained by genetic algorithm. The optimal cluster centers act as input to the SVM classifier and the kernel parameters are obtained. Finally, parameters of the kernel function are optimized by genetic algorithm, which help in classifying the skin lesions into various grades leading to early diagnosis of skin cancer.","abstract_has_math":false,"creators":["Panja, Sukanya"],"institution":null,"degree_name":"Master of Science in Electrical Engineering - (M.S.)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Atam P. Dhawan","Yun Q. Shi","Edwin Hou"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-01-31T08:00:00Z","date_published":"2015-01-31T08:00:00Z","updated_at":"2026-07-24T03:22:34Z","subjects":["Skin cancer screening","Surface features","Subsurface blood volume","Electrical and Electronics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/theses/219","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Atam P. Dhawan","Yun Q. 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Surface features, such as texture and pigmentation area from the surface, epi-illumination images of the skin lesions have been well correlated to detect skin cancer. An increase in the lesion's subsurface blood volume has been correlated to early diagnosis of malignant melanoma. A method for estimating the optimal features is obtained. The optimal features help in accurately classify the skin lesion in various grades. To make the process faster these optimal features are clustered. The optimal clusters are obtained by genetic algorithm. The optimal cluster centers act as input to the SVM classifier and the kernel parameters are obtained. Finally, parameters of the kernel function are optimized by genetic algorithm, which help in classifying the skin lesions into various grades leading to early diagnosis of skin cancer."]},{"key":"dc:title","label":"Title","values":["Computer aided analysis of skin lesions"]}]}],"canonical_facts":{"dc:contributor":["Atam P. Dhawan","Yun Q. Shi","Edwin Hou"],"dc:creator":["Panja, Sukanya"],"dc:description.abstract":["Effective screening to detect the skin cancer accurately in the early stage is essential for reducing the mortality of skin cancer. Surface features, such as texture and pigmentation area from the surface, epi-illumination images of the skin lesions have been well correlated to detect skin cancer. An increase in the lesion's subsurface blood volume has been correlated to early diagnosis of malignant melanoma. A method for estimating the optimal features is obtained. The optimal features help in accurately classify the skin lesion in various grades. To make the process faster these optimal features are clustered. The optimal clusters are obtained by genetic algorithm. The optimal cluster centers act as input to the SVM classifier and the kernel parameters are obtained. Finally, parameters of the kernel function are optimized by genetic algorithm, which help in classifying the skin lesions into various grades leading to early diagnosis of skin cancer."],"dc:identifier":["https://digitalcommons.njit.edu/theses/219"],"dc:subject":["Skin cancer screening","Surface features","Subsurface blood volume","Electrical and Electronics"],"dc:title":["Computer aided analysis of skin lesions"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Science in Electrical Engineering - (M.S.)"]},"updated_at":"2026-07-24T03:22:34Z"}